Texture, Shape, Order, and Relation Matter: A New Transformer Design for Sequential DeepFake Detection
This work addresses the emerging task of detecting manipulation sequences in DeepFakes, which is important for media forensics and security, but it appears incremental as it builds upon existing Transformer-based methods with dedicated design improvements.
The paper tackles the problem of sequential DeepFake detection by proposing a new Transformer design called TSOM, which incorporates texture, shape, order, and relation perspectives to improve performance, achieving state-of-the-art results in experiments.
Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. Existing methods typically formulate it as an image-to-sequence problem, employing conventional Transformer architectures. However, these methods lack dedicated design and consequently result in limited performance. As such, this paper describes a new Transformer design, called {TSOM}, by exploring three perspectives: Texture, Shape, and Order of Manipulations. Our method features four major improvements: \ding{182} we describe a new texture-aware branch that effectively captures subtle manipulation traces with a Diversiform Pixel Difference Attention module. \ding{183} Then we introduce a Multi-source Cross-attention module to seek deep correlations among spatial and sequential features, enabling effective modeling of complex manipulation traces. \ding{184} To further enhance the cross-attention, we describe a Shape-guided Gaussian mapping strategy, providing initial priors of the manipulation shape. \ding{185} Finally, observing that the subsequent manipulation in a sequence may influence traces left in the preceding one, we intriguingly invert the prediction order from forward to backward, leading to notable gains as expected. Building upon TSOM, we introduce an extended method, {TSOM++}, which additionally explores Relation of manipulations: \ding{186} we propose a new sequential contrastive learning scheme to capture relationships between various manipulation types in sequence, further enhancing the detection of manipulation traces. We conduct extensive experiments in comparison with several state-of-the-art methods, demonstrating the superiority of our method. The code has been released at https://github.com/OUC-VAS/TSOM.